The Internet and or other network systems may provide a unique opportunity to transmit for example complex images, typically large scale bit-maps, particularly those approaching photo-realistic levels, over large area and or distances. In common application, the images may be geographic, topographic, and or other highly detailed maps. The data storage requirements and often proprietary nature of such images could be such that conventional interests may be to transfer the images on an as-needed basis.
In conventional fixed-site applications, the image data may be transferred over a relatively high-bandwidth network to client computer systems that in turn, may render the image. Client systems may typically implement a local image navigation system to provide zoom and or pan functions based on user interaction. As well recognized problem with such conventional systems could be that full resolution image presentation may be subject to the inherent transfer latency of the network. Different conventional systems have been proposed to reduce the latency affect by transmitting the image in highly compressed formats that support progressive resolution build-up of the image within the current client field of view. Using a transform compressed image transfer function increases the field of the image that can be transferred over a fixed bandwidth network in unit time. Progressive image resolution transmission, typically using a differential resolution method, permits an approximate image to be quickly presented with image details being continuously added over time.
Tzou, in U.S. Pat. No. 4,698,689, describes a two-dimensional data transform system that supports transmission of differential coefficients to represent an image. Subsequent transmitted coefficient sets are progressively accumulated with prior transmitted sets to provide a succeedingly refined image. The inverse-transform function performed by the client computer is, however, highly compute intensive. In order to simplify the transform implementation and further reduce the latency of presenting any portion of an approximate image, images are sub-divided into a regular array. This enables the inverse-transform function on the client, which is time-critical, to deal with substantially smaller coefficient data sets. The array size in Tzou is fixed, which leads to progressively larger coefficient data sets as the detail level of the image increases. Consequently, there is an inherently increasing latency in resolving finer levels of detail.
An image visualization system proposed by Yap et al., U.S. Pat. No. 6,182,114, overcomes some of the foregoing problems. The Yap et al. system also employs a progressive encoding transform to compress the image transfer stream. The transform also operates on a subdivided image, but the division is indexed to the encoding level of the transform. The encoded transform coefficient data sets are, therefore, of constant size, which supports a modest improvement in the algorithmic performance of the inverse transform operation required on the client.
Yap et al. adds utilization of client image panning or other image pointing input information to support a foveation-based operator to influence the retrieval order of the subdivided image blocks. This two-dimensional navigation information is used to identify a foveal region that is presumed to be the gaze point of a client system user. The foveation operator defines the corresponding image block as the center point of an ordered retrieval of coefficient sets representing a variable resolution image. The gaze point image block represents the area of highest image resolution, with resolution reduction as a function of distance from the gaze point determined by the foveation operator. This technique thus progressively builds image resolution at the gaze point and succeedingly outward based on a relatively compute intensive function. Shifts in the gaze point can be responded to with relative speed by preferentially retrieving coefficient sets at and near the new foveal region.
Significant problems remain in permitting the convenient and effective use of complex images by many different types of client systems, even with the improvements provided by the various conventional systems. In particular, the implementation of conventional image visualization systems is generally unworkable for smaller, often dedicated or embedded, clients where use of image visualization would clearly be beneficial. Conventional approaches effectively presume that client systems have an excess of computing performance, memory and storage. Small clients, however, typically have restricted performance processors with possibly no dedicated floating-point support, little general purpose memory, and extremely limited persistent storage capabilities, particularly relative to common image sizes. A mobile computing device such as mobile phone, smart phone, and or personal digital assistant (PDA) is a characteristic small client. Embedded, low-cost kiosk and or automobile navigation systems are other typical examples. Such systems are not readily capable, if at all, of performing complex, compute-intensive Fourier or wavelet transforms, particularly within a highly restricted memory address space.
As a consequence of the presumption that the client is a substantial computing system, conventional image visualization systems also presume that the client is supported by a complete operating system. Indeed, many expect and require an extensive set of graphics abstraction layers to be provided by the client system to support the presentation of the delivered image data. In general, these abstraction layers are conventionally considered required to handle the mapping of the image data resolution to the display resolution capabilities of the client system. That is, resolution resolved image data provided to the client is unconstrained by any limitation in the client system to actually display the corresponding image. Consequently, substantial processor performance and memory can be conventionally devoted to handling image data that is not or cannot be displayed.
Another problem is that small clients are generally constrained to generally to very limited network bandwidths, particularly when operating under wireless conditions. Such limited bandwidth conditions may exist due to either the direct technological constraints dictated by the use of a low bandwidth data channel or indirect constraints imposed on relatively high-bandwidth channels by high concurrent user loads. Cellular connected PDAs and webphones are examples of small clients that are frequently constrained by limited bandwidth conditions. The conventionally realizable maximum network transmission bandwidth for such small devices may range from below one kilobit per second to several tens of kilobits per second. While Yap et al. states that the described system can work over low bandwidth lines, little more than utilizing wavelet-based data compression is advanced as permitting effective operation at low communications bandwidths. While reducing the amount of data that must be carried from the server to the client is significant, Yap et al. simply relies on the data packet transfer protocols to provide for an efficient transfer of the compressed image data. Reliable transport protocols, however, merely mask packet losses and the resultant, sometimes extended, recovery latencies. When such covered errors occur, however, the aggregate bandwidth of the connection is reduced and the client system can stall waiting for further image data to process.
Consequently, there remains a need for an image visualization system that can support small client systems, place few requirements on the supporting client hardware and software resources, and efficiently utilize low to very low bandwidth network connections.